• Journal of Information Technology Applications and Management
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• 제21권4호
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• pp.141-156
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• 2014

This study intended to evaluate the viability of cyber education in terms of learning satisfaction and learning achievement. The study integrated two research streams such as social presence model and learning environment model. Where the learning environment model emphasizes the components of learning aids, social presence model considers more deeply the relationships among peers and with instructors. These two research streams have been considered relatively independently. The study integrated these ideas and measured their reliabilities and validities. The results demonstrate that the two constructs are relevantly independent and both of these constructs are very important considerations for the success of cyber education. The study concludes that cyber education 2.0 requires more social presence factors than the learning environment factors such as technological development or new equipments.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2008년도 춘계 종합학술대회 논문집
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• pp.394-397
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• 2008

이러닝에서는 학생의 중도 포기율이 높기 때문에 학생의 학습의지를 어떻게 장려할 수 있는가가 매우 중요하다. 따라서 본 연구에서는 학생의 학습의지에 미치는 여러가지 영향요인 중 공동체의식을 주요 요인으로 파악하여 그 영향력을 살펴보고자 하였다. 아울러 이러한 공동체의식이 이러닝 교육기관에 대한 충성도에는 어떤 영향을 미치는지도 살펴보았다. 본 연구를 통해 공동체의식은 학습기관 일체감을 형성하고 이러한 학습기관 일체감은 학생의 학습의지와 학교에 대한 충성도를 증대시킨다는 것을 확인할 수 있었다.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• Asia pacific journal of information systems
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• 제18권4호
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• pp.105-130
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• 2008

Consumers of e-learning market differ from those of other markets in that they are replaced in a specific time scale. For example, e-learning contents aimed at highschool senior students cannot be consumed by a specific consumer over the designated period of time. Hence e-learning service providers need to attract new groups of students every year. Due to lack of information on products designed for continuously emerging consumers, the consumers face difficulties in making rational decisions in a short time period. Increased uncertainty of product purchase leads customers to herding behaviors to obtain information of the product from others and imitate them. Taking into consideration of these features of e-learning market, this study will focus on the online herding behavior in purchasing e-learning contents. There is no definite concept for e-learning. However, it is being discussed in a wide range of perspectives from educational engineering to management to e-business etc. Based upon the existing studies, we identify two main view-points regarding e-learning. The first defines e-learning as a concept that includes existing terminologies, such as CBT (Computer Based Training), WBT (Web Based Training), and IBT (Internet Based Training). In this view, e-learning utilizes IT in order to support professors and a part of or entire education systems. In the second perspective, e-learning is defined as the usage of Internet technology to deliver diverse intelligence and achievement enhancing solutions. In other words, only the educations that are done through the Internet and network can be classified as e-learning. We take the second definition of e-learning for our working definition. The main goal of this study is to investigate what factors affect consumer intention to purchase e-learning contents and to identify the differential impact of the factors between consumers with purchase experience and those without the experience. To accomplish the goal of this study, it focuses on herding behavior and perceived usefulness as antecedents to behavioral intention. The proposed research model in the study extends the Technology Acceptance Model by adding herding behavior and usability to take into account the unique characteristics of e-learning content market and e-learning systems use, respectively. The current study also includes consumer experience with e-learning content purchase because the previous experience is believed to affect purchasing intention when consumers buy experience goods or services. Previous studies on e-learning did not consider the characteristics of e-learning contents market and the differential impact of consumer experience on the relationship between the antecedents and behavioral intention, which is the target of this study. This study employs a survey method to empirically test the proposed research model. A survey questionnaire was developed and distributed to 629 informants. 528 responses were collected, which consist of potential customer group (n = 133) and experienced customer group (n = 395). The data were analyzed using PLS method, a structural equation modeling method. Overall, both herding behavior and perceived usefulness influence consumer intention to purchase e-learning contents. In detail, in the case of potential customer group, herding behavior has stronger effect on purchase intention than does perceived usefulness. However, in the case of shopping-experienced customer group, perceived usefulness has stronger effect than does herding behavior. In sum, the results of the analysis show that with regard to purchasing experience, perceived usefulness and herding behavior had differential effects upon the purchase of e-learning contents. As a follow-up analysis, the interaction effects of the number of purchase transaction and herding behavior/perceived usefulness on purchase intention were investigated. The results show that there are no interaction effects. This study contributes to the literature in a couple of ways. From a theoretical perspective, this study examined and showed evidence that the characteristics of e-learning market such as continuous renewal of consumers and thus high uncertainty and individual experiences are important factors to be considered when the purchase intention of e-learning content is studied. This study can be used as a basis for future studies on e-learning success. From a practical perspective, this study provides several important implications on what types of marketing strategies e-learning companies need to build. The bottom lines of these strategies include target group attraction, word-of-mouth management, enhancement of web site usability quality, etc. The limitations of this study are also discussed for future studies.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• 통상정보연구
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• 제13권4호
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• pp.81-100
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• 2011

스마트 기기 보급에 따라 NFC 기술에 대한 관심이 날이 갈수록 증대되고 있다. 본 연구에서는 NFC 기반 상거래가 성공하기 위해서는 기술적인 차원의 접근보다는 비즈니스 모델차원의 접근이 필요하다는 가정 하에, NFC 상거래 비즈니스 모델이 성공하기 위하여 필요한 요인들이 무엇인지 제시하고자 한다. 이를 위하여 NFC와 비즈니스 모델, 사례연구방법론 주요 개념에 대하여 문헌 연구를 통하여 고찰하고, 대표적인 NFC 상거래 사례들을 선택하고, 해당 사례의 성공과 실패에 영향을 미치는 주요 요인들을 도출하였다. 도출된 요인을 기반으로 ID3기반의 귀납적 추론 기법을 적용하기 위한 사례테이블을 작성하고, 의사결정나무(Decision Tree)를 도출하여, NFC 상거래 비즈니스 모델이 성공하기 위하여 필요한 부분에 대한 전략적 시사점(Strategic Implications)을 제시하였다.

• 컴퓨터교육학회논문지
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• 제14권1호
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• pp.81-89
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• 2011

사이버가정학습은 학습자의 학습 영역을 가정으로 확산해 가는 것이 근본 취지이지만, 학교교육과 연계되지 못하면 그 실효성이 미미하다는 것이 연구학교 교사들의 지적이었다. 본 연구에서는 사이버가정학습을 활용함에 있어서 학교교육과 연계한 블렌디드 러닝(Blended-learning) 교육방법과 학습 스타일 등 학습자의 학습 요소가 학력 향상에 미치는 영향력을 비교해보았다. 사전검사결과 사회과의 학력에 차이가 없는 두 개 학급을 선정해 1학기동안 순수한 사이버학습과 블렌디드 학습의 두 형태로 운영을 해보고, 학력 향상에 미친 요소들의 영향력을 통계적으로 검정해보았다. 그 결과 학습 방법이 가장 큰 영향력을 보여주었고, 다음으로 Kolb의 학습 스타일에서 구체적 경험을 중시하는 학습 스타일과 반성적 관찰을 중시하는 학습 스타일의 영향력이 통계적으로 유의한 차이를 보여주었다. 연구 결과 사이버가정학습의 효과적 운영을 위해 학교교육과 적극적으로 연계되어 활용되어야 하며, 학습자들의 학습 스타일에 맞추어 적합한 학습 콘텐츠가 마련되어야 함을 보여주었다.

• Asia pacific journal of information systems
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• 제20권2호
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• pp.1-21
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• 2010

e-Government is an innovative phenomenon around the globe for refurbishing public administration. Research to date, however, has been deficient in empirical studies of the factors that emerge out of the interplay of structure and human interactions responsible for successful implementation of e-Government projects. From the perspective of structuration theory, this study examines and explains the impact of this interplay in the implementation of an e-Government system, eSeva for local administration at one of the Indian state, The results suggest that the success of implementation of e-Government projects can be conceptualized as the outcome of persisting constructive interrelationships among the human, programmatic and institutional elements of e-Government and the critical learning and adaptation as a result of efforts at the structuration level.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제13권1호
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• pp.28-34
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• 2024

이러닝은 학습자 자율에 의해 진행되므로 성공적인 학습을 위해서는 지속적인 참여를 위한 학습동기가 무엇보다 중요하다. 평생교육에 참여하는 성인학습자가 증가하면서 이들의 학습참여와 참여에 영향을 미치는 학습동기에 대한 연구가 필요하다. 학습동기와 학습참여간의 관계를 설명하는 기대가치이론과 자기주도학습이론을 바탕으로 학습동기의 구성요소(학습가치, 비용, 자기조절, 일정관리)가 학습참여에 미치는 영향을 분석하였다. 이러닝 프로그램을 MoodleCloud에 구축하였고, 학습자는 설문에 답한 후 학습을 진행하였다. 학습 진행 과정에서 수집한 로그데이터로 산출한 학습참여점수와 설문 응답 데이터를 이용하여 회귀분석을 실시하였다. 분석 결과 성인학습자의 이러닝 학습참여에 유의한 영향을 미치는 동기요인은 학습가치와 일정관리이며, 학습가치에 대한 생각은 남녀 간의 차이가 있는 것으로 나타났다. 즉 성인학습자가 이러닝 학습 프로그램의 가치를 높게 인지할수록, 학습자 스스로 일정관리 능력이 있을수록 학습에 많이 참여한다고 할 수 있다. 이 연구 결과는 학습자와 교수자의 교수·학습전략 수립에 활용될 수 있고, 궁극적으로는 이러닝 중도 탈락을 방지하는 데 도움이 될 수 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권3호
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.